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diploma project - References

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16 Jan 2018

Tasks

  1. 调研边缘检测和图像分割算法的发展历程

  2. 使用传统的基于特征的方法对图像进行分割和边缘检测

  3. 参考最新的使用深度学习的边缘检测算法,设计自己的模型结构

  4. 对比各种算法在性能上的差异,提出改进意见

  5. 探究如何在小数据集上取得良好的分割效果,使用迁移学习方法提高模型在不同训练数据、测试数据上迁移的能力

References

[1]. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014), arXiv:1409.1556 [cs.CV]

[2]. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation (2014), arXiv:1411.4038 [cs.CV]

[3]. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for ac- curate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)

[4]. G. Bertasius, J. Shi, L. Torresani, “Deepedge: A multiscale bifurcated deep network for top-down contour detection”, CVPR, 2015.

[5]. Saining Xie, Zhuowen Tu, “Holistically-Nested Edge Detection”, ICCV, 2015.

[6]. Jimei Yang, Brain Price, Scott Chen, “Object Contour Detection with a Fully Convolutional Encoder-Decoder Network”, arXiv:1603.04530v1 [cs.CV]

[7]. Yun Liu, Ming-Ming Cheng, Xiaowei Hu, Kai Wang, Xiang Bai, “Richer Convolutional Features for Edge Detection”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3000-3009.

[8]. Zhiding Yu, Chen Feng, Ming-Yu Liu, Srikumar Ramalingam, “CASENet: Deep Category-Aware Semantic Edge Detection”, arXiv:1705.09759 [cs.CV]

[9]. Song Yuheng, Yan Hao, “Image Segmentation Algorithms Overview”, arXiv:1707.02051 [cs.CV]

[10]. Olaf Ronneberger, Philipp Fischer, Thomas Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, arXiv:1505.04597 [cs.CV]

[11]. Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick, “Mask R-CNN”, arXiv:1703.06870v2 [cs.CV]


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